Use when writing E2E tests with Playwright, setting up test infrastructure, or debugging flaky browser tests. Invoke to write test scripts, create page objects, configure test fixtures, set up reporters, add CI integration, implement API mocking, or perform visual regression testing. Trigger terms: Playwright, E2E test, end-to-end, browser testing, automation, UI testing, visual testing, Page Object Model, test flakiness.

Execute comprehensive web application security testing using Burp Suite's integrated toolset, including HTTP traffic interception and modification, request analysis and replay, automated vulnerability scanning, and manual testing workflows.

Create diagnostic bundles for Granola support requests. Use when preparing support tickets, collecting system/audio/network info, or diagnosing complex issues that require Granola support team assistance. Trigger: "granola debug", "granola diagnostics", "granola support bundle", "granola logs", "granola system info".

Structure and data sources for the weekly inventory report. Load this when the task is "weekly report", "Monday report", or "summarize inventory status".

Setup synthetic monitoring for proactive performance tracking including uptime checks, transaction monitoring, and API health. Use when implementing availability monitoring or tracking critical user journeys. Trigger with phrases like "setup synthetic monitoring", "monitor uptime", or "configure health checks".

Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

| Nuxt 4 core framework fundamentals: project setup, configuration, routing, SEO, error handling, and directory structure. Use when: creating new Nuxt 4 projects, configuring nuxt.config.ts, setting up routing and middleware, implementing SEO with useHead/useSeoMeta, handling errors with error.vue and NuxtErrorBoundary, or understanding Nuxt 4 directory structure.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Add a "code mode" tool to an existing MCP server so LLMs can write small processing scripts that run against large API responses in a sandboxed runtime — only the script's compact output enters the LLM context window. Use this skill whenever someone wants to add code mode, context reduction, script execution, sandbox execution, or LLM-generated-code processing to an MCP server. Also trigger when users mention reducing token usage, shrinking API responses, running user-provided code safely, or adding a code execution tool to their MCP server — in any language (TypeScript, Python, Go, Rust, etc.).

YouTube Ads specific analysis covering campaign types, creative quality, audience targeting, and measurement. Evaluates video ad performance across skippable in-stream, non-skippable, bumper, Shorts, Demand Gen, and Connected TV (CTV) formats. Covers VAC→Demand Gen migration, Shorts creative requirements, CTV shoppable ads, and frequency capping. Use when user says YouTube Ads, video ads, pre-roll, bumper ads, skippable in-stream, YouTube campaign, YouTube Shorts ads, Demand Gen, VAC, or CTV YouTube ads.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Automatically generate TLA+ specifications from program code, repositories, or system implementations. Use when asked to generate TLA+ spec, create TLA+ specification from code, convert program to TLA+, formalize system in TLA+, extract TLA+ model from code, or when working with formal specification of concurrent systems, distributed systems, protocols, algorithms, or state machines that need to be verified.

Generate unit tests with proper mocking for Python (unittest.mock/pytest) or Java (Mockito/JUnit) code. Use when users request test generation, unit tests with mocks, or testing code that has external dependencies like database calls, API requests, file I/O, or network operations. Automatically identifies dependencies to mock and creates executable, maintainable test code.

Generates complete modules and components from interface contracts and specifications. Use when Claude needs to build larger software components, implement interfaces, create service layers, or generate complete modules with multiple related classes. Supports Python (with type hints and protocols) and Java (with interfaces and abstract classes). Trigger when users request "implement this interface", "generate a module for", "create a service layer", "build a component that implements", or provide interface definitions that need implementation.

Generate integration tests for multiple interacting components in Python. Use when testing interactions between: (1) Multiple services or APIs (REST/GraphQL endpoints, microservices), (2) Database operations with repositories/ORMs (SQLAlchemy, Django ORM), (3) External services (payment gateways, email services, third-party APIs), (4) Message queues and event-driven systems, (5) Full stack workflows (API + database + business logic). Provides test structure templates, fixtures, test data builders, and patterns for pytest-based integration testing.

Automatically repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

Analyzes and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance improvements.

Execute Juicebox production checklist. Trigger: "juicebox production", "deploy juicebox".